Modeling of Key Quality Indicators for End-to-End Network Management: Preparing for 5G
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
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| _version_ | 1866911775021072384 |
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| author | Herrera-Garcia, Ana Fortes, Sergio Baena, Eduardo Mendoza, Jessica Baena, Carlos Barco, Raquel |
| author_facet | Herrera-Garcia, Ana Fortes, Sergio Baena, Eduardo Mendoza, Jessica Baena, Carlos Barco, Raquel |
| contents | Thanks to evolving cellular telecommunication networks, providers can deploy a wide range of services. Soon, 5G mobile networks will be available to handle all types of services and applications for vast numbers of users through their mobile equipment. To effectively manage new 5G systems, end-to-end (E2E) performance analysis and optimization will be key features. However, estimating the end-user experience is not an easy task for network operators. The amount of end-user performance information operators can measure from the network is limited, complicating this approach. Here we explore the calculation of service metrics [known as key quality indicators (KQIs)] from classic low-layer measurements and parameters. We propose a complete machine-learning (ML) modeling framework. This system's low-layer metrics can be applied to measure service-layer performance. To assess the approach, we implemented and evaluated the proposed system on a real cellular network testbed. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_07071 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Modeling of Key Quality Indicators for End-to-End Network Management: Preparing for 5G Herrera-Garcia, Ana Fortes, Sergio Baena, Eduardo Mendoza, Jessica Baena, Carlos Barco, Raquel Networking and Internet Architecture Thanks to evolving cellular telecommunication networks, providers can deploy a wide range of services. Soon, 5G mobile networks will be available to handle all types of services and applications for vast numbers of users through their mobile equipment. To effectively manage new 5G systems, end-to-end (E2E) performance analysis and optimization will be key features. However, estimating the end-user experience is not an easy task for network operators. The amount of end-user performance information operators can measure from the network is limited, complicating this approach. Here we explore the calculation of service metrics [known as key quality indicators (KQIs)] from classic low-layer measurements and parameters. We propose a complete machine-learning (ML) modeling framework. This system's low-layer metrics can be applied to measure service-layer performance. To assess the approach, we implemented and evaluated the proposed system on a real cellular network testbed. |
| title | Modeling of Key Quality Indicators for End-to-End Network Management: Preparing for 5G |
| topic | Networking and Internet Architecture |
| url | https://arxiv.org/abs/2402.07071 |